102 research outputs found
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
With the widespread use of biometric systems, the demographic bias problem
raises more attention. Although many studies addressed bias issues in biometric
verification, there are no works that analyze the bias in presentation attack
detection (PAD) decisions. Hence, we investigate and analyze the demographic
bias in iris PAD algorithms in this paper. To enable a clear discussion, we
adapt the notions of differential performance and differential outcome to the
PAD problem. We study the bias in iris PAD using three baselines (hand-crafted,
transfer-learning, and training from scratch) using the NDCLD-2013 database.
The experimental results point out that female users will be significantly less
protected by the PAD, in comparison to males.Comment: accepted for publication at EUSIPCO202
Beyond Identity: What Information Is Stored in Biometric Face Templates?
Deeply-learned face representations enable the success of current face
recognition systems. Despite the ability of these representations to encode the
identity of an individual, recent works have shown that more information is
stored within, such as demographics, image characteristics, and social traits.
This threatens the user's privacy, since for many applications these templates
are expected to be solely used for recognition purposes. Knowing the encoded
information in face templates helps to develop bias-mitigating and
privacy-preserving face recognition technologies. This work aims to support the
development of these two branches by analysing face templates regarding 113
attributes. Experiments were conducted on two publicly available face
embeddings. For evaluating the predictability of the attributes, we trained a
massive attribute classifier that is additionally able to accurately state its
prediction confidence. This allows us to make more sophisticated statements
about the attribute predictability. The results demonstrate that up to 74
attributes can be accurately predicted from face templates. Especially
non-permanent attributes, such as age, hairstyles, haircolors, beards, and
various accessories, found to be easily-predictable. Since face recognition
systems aim to be robust against these variations, future research might build
on this work to develop more understandable privacy preserving solutions and
build robust and fair face templates.Comment: To appear in IJCB 202
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
Face quality assessment aims at estimating the utility of a face image for
the purpose of recognition. It is a key factor to achieve high face recognition
performances. Currently, the high performance of these face recognition systems
come with the cost of a strong bias against demographic and non-demographic
sub-groups. Recent work has shown that face quality assessment algorithms
should adapt to the deployed face recognition system, in order to achieve
highly accurate and robust quality estimations. However, this could lead to a
bias transfer towards the face quality assessment leading to discriminatory
effects e.g. during enrolment. In this work, we present an in-depth analysis of
the correlation between bias in face recognition and face quality assessment.
Experiments were conducted on two publicly available datasets captured under
controlled and uncontrolled circumstances with two popular face embeddings. We
evaluated four state-of-the-art solutions for face quality assessment towards
biases to pose, ethnicity, and age. The experiments showed that the face
quality assessment solutions assign significantly lower quality values towards
subgroups affected by the recognition bias demonstrating that these approaches
are biased as well. This raises ethical questions towards fairness and
discrimination which future works have to address.Comment: Accepted at IJCB202
Performing Realistic Workout Activity Recognition on Consumer Smartphones
Smartphones have become an essential part of our lives. Especially its computing power
and its current specifications make a modern smartphone a powerful device for human activity
recognition tasks. Equipped with various integrated sensors, a modern smartphone can be leveraged
for lots of smart applications. We already investigated the possibility of using an unmodified
commercial smartphone to recognize eight strength-based exercises. App-based workouts have
become popular in the last few years. The advantage of using a mobile device is that you can practice
anywhere at anytime. In our previous work, we proved the possibility of turning a commercial
smartphone into an active sonar device to leverage the echo reflected from exercising movement
close to the device. By conducting a test study with 14 participants, we showed the first results for
cross person evaluation and the generalization ability of our inference models on disjoint participants.
In this work, we extended another model to further improve the model generalizability and provided
a thorough comparison of our proposed system to other existing state-of-the-art approaches. Finally,
a concept of counting the repetitions is also provided in this study as a parallel task to classification
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face image quality is an important factor to enable high performance face
recognition systems. Face quality assessment aims at estimating the suitability
of a face image for recognition. Previous work proposed supervised solutions
that require artificially or human labelled quality values. However, both
labelling mechanisms are error-prone as they do not rely on a clear definition
of quality and may not know the best characteristics for the utilized face
recognition system. Avoiding the use of inaccurate quality labels, we proposed
a novel concept to measure face quality based on an arbitrary face recognition
model. By determining the embedding variations generated from random
subnetworks of a face model, the robustness of a sample representation and
thus, its quality is estimated. The experiments are conducted in a
cross-database evaluation setting on three publicly available databases. We
compare our proposed solution on two face embeddings against six
state-of-the-art approaches from academia and industry. The results show that
our unsupervised solution outperforms all other approaches in the majority of
the investigated scenarios. In contrast to previous works, the proposed
solution shows a stable performance over all scenarios. Utilizing the deployed
face recognition model for our face quality assessment methodology avoids the
training phase completely and further outperforms all baseline approaches by a
large margin. Our solution can be easily integrated into current face
recognition systems and can be modified to other tasks beyond face recognition.Comment: Accepted at CVPR202
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
Current face recognition systems achieve high progress on several benchmark
tests. Despite this progress, recent works showed that these systems are
strongly biased against demographic sub-groups. Consequently, an easily
integrable solution is needed to reduce the discriminatory effect of these
biased systems. Previous work mainly focused on learning less biased face
representations, which comes at the cost of a strongly degraded overall
recognition performance. In this work, we propose a novel unsupervised fair
score normalization approach that is specifically designed to reduce the effect
of bias in face recognition and subsequently lead to a significant overall
performance boost. Our hypothesis is built on the notation of individual
fairness by designing a normalization approach that leads to treating similar
individuals similarly. Experiments were conducted on three publicly available
datasets captured under controlled and in-the-wild circumstances. Results
demonstrate that our solution reduces demographic biases, e.g. by up to 82.7%
in the case when gender is considered. Moreover, it mitigates the bias more
consistently than existing works. In contrast to previous works, our fair
normalization approach enhances the overall performance by up to 53.2% at false
match rate of 0.001 and up to 82.9% at a false match rate of 0.00001.
Additionally, it is easily integrable into existing recognition systems and not
limited to face biometrics.Comment: Accepted in Pattern Recognition Letter
Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems
Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight
ExerTrack - Towards Smart Surfaces to Track Exercises
The concept of the quantified self has gained popularity in recent years with the hype of
miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other
fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running,
or cycling can be accurately recognized using wearable devices. However whole-body exercises
such as push-ups, bridges, and sit-ups are performed on the ground and thus cannot be precisely
recognized by wearing only one accelerometer. Thus, a floor-based approach is preferred for
recognizing whole-body activities. Computer vision techniques on image data also report high
recognition accuracy; however, the presence of a camera tends to raise privacy issues in public areas.
Therefore, we focus on combining the advantages of ubiquitous proximity-sensing with non-optical
sensors to preserve privacy in public areas and maintain low computation cost with a sparse sensor
implementation. Our solution is the ExerTrack, an off-the-shelf sports mat equipped with eight
sparsely distributed capacitive proximity sensors to recognize eight whole-body fitness exercises
with a user-independent recognition accuracy of 93.5 % and a user-dependent recognition accuracy
of 95.1 % based on a test study with 9 participants each performing 2 full sessions. We adopt a
template-based approach to count repetitions and reach a user-independent counting accuracy of
93.6 %. The final model can run on a Raspberry Pi 3 in real time. This work includes data-processing of
our proposed system and model selection to improve the recognition accuracy and data augmentation
technique to regularize the network
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